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What to Do When AI Gets Your Brand Wrong

You ask ChatGPT about your company and the answer is close, but not right. It lists a product you discontinued two years ago. It says you only serve one country when you serve five. It quotes a price from an old blog post, or confuses you with a company that has a similar name. Sometimes it invents a feature you never built.

This is more common than most teams realize, and it matters. A buyer who reads a wrong description in an AI answer rarely double checks it. They simply move on to the competitor the assistant described clearly. The good news is that AI errors about your brand are usually not random. They have causes, and most of those causes are fixable.

Why AI assistants get brand facts wrong

AI models build their picture of your brand from what they have read across the web, plus whatever they can retrieve live when they search. When that picture is wrong, it usually comes from one of a few sources:

• Outdated content. Old press releases, archived pricing pages, and stale directory listings still exist, and models may have learned from them.
• Thin information. If there is not much written about you, the model fills the gaps with guesses based on similar companies.
• Conflicting sources. Your website says one thing, your LinkedIn says another, and a review site says a third. The model picks one, or blends them.
• Name confusion. A brand name shared with another company, product, or place can cause the model to merge two identities.
• Training lag. A model's core knowledge has a cutoff date, so recent launches, rebrands, or price changes may simply be missing.

AI rarely makes up a brand story from nothing. It repeats, mixes, or fills in what the web already says about you, so the fix starts with what the web says.

Step 1: Document exactly what is wrong

Before you change anything, get specific. Ask ChatGPT, Claude, and Gemini the questions a real customer would ask: what does this company do, who is it for, how much does it cost, what are the alternatives. Write down every inaccurate statement, which assistant said it, and the exact wording. Patterns appear quickly. If all three assistants repeat the same old price, there is almost certainly a page somewhere that still shows it.

Step 2: Fix the source, not the symptom

You cannot edit a model's answer directly, but you can change the material it learns from and retrieves. Match each error to its likely cause:

What AI says	Likely cause	What to do
Old prices or plans	Archived pages, old blog posts, third-party listings	Update or redirect old pages, refresh directory and comparison site entries
Wrong category or audience	Vague homepage copy	State plainly what you do, for whom, and where
Discontinued products	Stale product pages still indexed	Remove, redirect, or clearly mark them as retired
Mixed up with another company	Similar names, inconsistent naming	Use one consistent brand name, add location and category context
Missing recent launches	Training cutoff, little coverage	Publish clear announcements and earn mentions on trusted sites

Step 3: Publish one clear source of truth

Give AI a page it can rely on. An "About" or "Company facts" page that states your name, what you do, who you serve, where you operate, your main products, and your pricing approach in short, plain sentences is easy for a model to read and quote. Keep it current, and make sure the same facts appear on your homepage, your LinkedIn page, and your main directory profiles. Consistency is what turns one page into a trusted signal.

Step 4: Correct the third-party record

Many wrong answers come from sites you do not own. Review platforms, industry directories, comparison articles, Wikipedia, and old news coverage all feed AI answers. Claim and update your profiles, ask publishers to correct factual errors in older articles, and refresh listings that still show your old name, address, or offering. Each corrected source reduces the weight of the wrong version.

Step 5: Use the feedback tools, but do not rely on them

Most assistants let users flag a bad answer with a thumbs down or a feedback note. It is worth doing, especially for clearly harmful errors, but it is not a reliable fix on its own. Treat it as a small extra, not the plan. Durable change comes from improving the information the models see.

Step 6: Check again, and keep checking

Changes do not show up overnight. Assistants that search the web live may reflect fixes within weeks, while a model's built-in knowledge only updates when a new version is trained. That is why it pays to recheck the same questions on a regular schedule and compare answers over time. You will see which errors disappear first, which persist, and whether new ones creep in after a rebrand or a price change.

• Recheck your core questions monthly, and after any major launch or change.
• Track accuracy, not just whether you are mentioned.
• Note which assistant is slowest to update, and focus on the sources it seems to use.

An AI that mentions you inaccurately can do more damage than one that does not mention you at all. Treat accuracy as part of your visibility, fix the sources behind the errors, and keep measuring until the answers match reality.